machine learning

Abstract: Advanced machine learning methods could be useful to obtain novel insights into some challenging nanomechanical problems. In this work, we employed artificial neural networks to predict the fracture stress of defective graphene samples. First, shallow neural networks were used to predict the fracture stress, which depends on the temperature, vacancy concentration, strain rate, and loading direction.

Dear Colleagues,

As part of the IMECE 2020 (November 13-19, 2020, Portland, Oregon), we are organizing a topic on “Data-Enabled Predictive Modeling, Machine Learning, and Uncertainty Quantification in Computational Mechanics.” It is listed in Track 12: Mechanics of Solids, Structures, and Fluids: https://event.asme.org/IMECE/Program/Tracks-Topics.

We have an open PhD position on machine learning enhanced multi-scale modelling of textile composites. The following link provides more information about the project, and the details of the application process. Please keep in mind that only applications sent through the online application system will be evaluated.

Description of the PhD project, and how to apply

Machine learning (ML) in Mechanics is a fascinating and timely topic. This article follows from a kind invitation to provide some thoughts about the use of ML algorithms to solve mechanics problems by overviewing my past and current research efforts along with students and collaborators in this field. A brief introduction on ML is initially provided for the colleagues not familiar with the topic, followed by a section about the usefulness of ML in Mechanics, and finally I will reflect on the challenges and opportunities in this field.

Dear colleagues,

Deadline to submit your abstract to ICTAM2020 and WCCM2020 is fast approacing (January 20 & 15, respectively). If you are working with machine learning, uncertainty quantification, optimization or a related topic, consider the following symposia:

Biomechanics of Traumatic Brain Injury

Measuring forming limit diagrams (FLDs) is a time consuming and expensive process. Machine learning (ML) methods are a promising route to predict FLD of aluminium alloys. In the present work, we developed a machine learning (ML) based tool to establish the relationships between alloy composition / thermomechanical processing route to the material's FLD.

Dear colleagues, 

we would like to make you aware of the topical session 

"Data driven materials science"

which is part of the MM program during the DPG Spring Meeting 2020. The latter takes place March 15-20, 2020, in Dresden.  

If you are performing experiments or simulations in this emerging field, you are most welcome to contribute your abstract. You can find the session at the bottom of the list "Themenbereiche" on the abstract submission webpage